Multiple discriminators on each scale, namely, \(D^s, D^m, D^l\) , are used in this work.
These discriminators have the same architectures across all scales.
The input for each discriminator comprises concatenated output and input images.
For images with lower resolution, the input images are downsampled from the original resolution to match the output resolution.
We predict \(N \times N\)  patch of images instead of single logits.
To further improve stability for training, we employ normalization operations.
Normalization in GAN helps the network eliminate covariance shift.
Batch normalization [1]} is a common normalization method, but it suffers from stability problems when the batch size is small.
We use a batch size of 1 across all scales to reduce the computation requirements. Therefore, the batch normalization method is not suitable for this task; we use the instance normalization [2]} instead.
